I have this function to find the best route:
from ortools.constraint_solver import pywrapcp, routing_enums_pb2
import numpy as np
def sin_restriccion(matriz_input, indices_input):
# Convertir el DataFrame a una matriz de distancias
distance_matrix = matriz_input
locations = indices_input
# Crear el modelo de datos
def create_data_model():
data = {}
data['distance_matrix'] = distance_matrix
data['num_vehicles'] = 1
data['depot'] = 0 # Puedes ajustar el depot según sea necesario
return data
# Crear el modelo de datos
data = create_data_model()
# Crear el gestor de rutas
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
data['num_vehicles'], data['depot'])
# Crear el modelo de rutas
routing = pywrapcp.RoutingModel(manager)
# Crear la función de distancia
def distance_callback(from_index, to_index):
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data['distance_matrix'][from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
# Definir el costo de la distancia
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# Eliminar la restricción de retorno al depot
routing.SetFixedCostOfAllVehicles(0)
end_index = manager.NodeToIndex(data['depot'])
routing.AddDisjunction([end_index], 1000000) # Penalización alta para no regresar al depot
# Definir parámetros de búsqueda
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
search_parameters.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
search_parameters.time_limit.seconds = 10 # Ajustar el límite de tiempo según sea necesario
# Resolver el problema
solution = routing.SolveWithParameters(search_parameters)
# Preparar el resultado como un diccionario
if solution:
index = routing.Start(0)
route_dict = {}
step = 1
total_distance = 0
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
route_dict[step] = locations[node_index]
next_index = solution.Value(routing.NextVar(index))
total_distance += distance_callback(index, next_index)
index = next_index
step += 1
# Imprimir distancia total recorrida
print(f'Distancia total recorrida: {total_distance}')
return route_dict
else:
print('No hay solución')
return 'No hay solución'
# Ejemplo de uso con la matriz proporcionada
matriz = np.array([
[0.0, 22.3, 1965.4, 2108.5],
[500.0, 0.0, 1611.7, 2130.8], # B a A es 500 en lugar de 22.3
[2033.1, 2080.6, 0.0, 2267.8],
[2037.2, 2084.7, 2189.1, 0.0]
])
indices = ['A', 'B', 'C', 'D']
# Llamada a la función
ruta_optima = sin_restriccion(matriz, indices)
print(ruta_optima)
But the result is: {1: 'A', 2: 'D', 3: 'C', 4: 'B'}
It is not too hard to see that A -> B -> C -> D is better that the result. Is there any configuration I am missing? Or its just how ortool works, not always finding the shortest solution?
I am not an expert with ortool so there may be some things I am ignoring.
the transit callback must return an int64_t, otherwise your floating point values will result to an undefind behaviour. (ed In the SWIG bridge between Python and C++, Python's floating values are converted to int64_t).
ref:
The routing RegisterTransitCallback() declaration:
https://github.com/google/or-tools/blob/2c333f58a37d7c75d29a58fd772c9b3f94e2ca1c/ortools/constraint_solver/routing.h#L654-L658
Typedef of RoutingTransitCallback:
https://github.com/google/or-tools/blob/2c333f58a37d7c75d29a58fd772c9b3f94e2ca1c/ortools/constraint_solver/routing_types.h#L49-L50
In your sample you could multiply all your value per 10 to have integer values, and divide them by 10 in your print/display function...